OpenRaiser/Intern-Atlas huggingface dataset:
Feels Bad Man: Dissecting Automated Hateful Meme Detection Through the Lens of Facebook's Challenge
Venue: arXiv cs.CY 2022
Year: 2022

Internet memes have become a dominant method of communication; at the same time, however, they are also increasingly being used to advocate extremism and foster derogatory beliefs. Nonetheless, we do not have a firm understanding as to which perceptual aspects of memes cause this phenomenon. In this work, we assess the efficacy of current state-of-the-art multimodal machine learning models toward hateful meme detection, and in particular with respect to their generalizability across platforms. We use two benchmark datasets comprising 12,140 and 10,567 images from 4chan's "Politically Incorrect" board (/pol/) and Facebook's Hateful Memes Challenge dataset to train the competition's top-ranking machine learning models for the discovery of the most prominent features that distinguish viral hateful memes from benign ones. We conduct three experiments to determine the importance of multimodality on classification performance, the influential capacity of fringe Web communities on mainstream social platforms and vice versa, and the models' learning transferability on 4chan memes. Our experiments show that memes' image characteristics provide a greater wealth of information than its textual content. We also find that current systems developed for online detection of hate speech in memes necessitate further concentration on its visual elements to improve their interpretation of underlying cultural connotations, implying that multimodal models fail to adequately grasp the intricacies of hate speech in memes and generalize across social media platforms.
The Development and Prospect of Code Clone
Venue: arXiv cs.SE 2022
Year: 2022

The application of code clone technology accelerates code search, improves code reuse efficiency, and assists in software quality assessment and code vulnerability detection. However, the application of code clones also introduces software quality issues and increases the cost of software maintenance. As an important research field in software engineering, code clone has been extensively explored and studied by researchers, and related studies on various sub-research fields have emerged, including code clone detection, code clone evolution, code clone analysis, etc. However, there lacks a comprehensive exploration of the entire field of code clone, as well as an analysis of the trend of each sub-research field. This paper collects related work of code clones in the past ten years. In summary, the contributions of this paper mainly include: (1) summarize and classify the sub-research fields of code clone, and explore the relative popularity and relation of these sub-research fields; (2) analyze the overall research trend of code clone and each sub-research field; (3) compare and analyze the difference between academy and industry regarding code clone research; (4) construct a network of researchers, and excavate the major contributors in code clone research field; (5) The list of popular conferences and journals was statistically analyzed. The popular research directions in the future include clone visualization, clone management, etc. For the clone detection technique, researchers can optimize the scalability and execution efficiency of the method, targeting particular clone detection tasks and contextual environments, or apply the technology to other related research fields continuously.
Finding Representative Sampling Subsets in Sensor Graphs using Time Series Similarities
Venue: arXiv cs.SI 2022
Year: 2022

With the increasing use of IoT-enabled sensors, it is important to have effective methods for querying the sensors. For example, in a dense network of battery-driven temperature sensors, it is often possible to query (sample) just a subset of the sensors at any given time, since the values of the non-sampled sensors can be estimated from the sampled values. If we can divide the set of sensors into disjoint so-called representative sampling subsets that each represent the other sensors sufficiently well, we can alternate the sampling between the sampling subsets and thus, increase battery life significantly. In this paper, we formulate the problem of finding representative sampling subsets as a graph problem on a so-called sensor graph with the sensors as nodes. Our proposed solution, SubGraphSample, consists of two phases. In Phase-I, we create edges in the sensor graph based on the similarities between the time series of sensor values, analyzing six different techniques based on proven time series similarity metrics. In Phase-II, we propose two new techniques and extend four existing ones to find the maximal number of representative sampling subsets. Finally, we propose AutoSubGraphSample which auto-selects the best technique for Phase-I and Phase-II for a given dataset. Our extensive experimental evaluation shows that our approach can yield significant battery life improvements within realistic error bounds.
Schedule-based Analysis of Transmission Risk in Public Transportation Systems
Venue: arXiv cs.SI 2022
Year: 2022

Airborne diseases, including COVID-19, raise the question of transmission risk in public transportation systems. However, quantitative analysis of the effectiveness of transmission risk mitigation methods in public transportation is lacking. The paper develops a transmission risk modeling framework based on the Wells-Riley model using as inputs transit operating characteristics, schedule, Origin-Destination (OD) demand, and virus characteristics. The model is sensitive to various factors that operators can control, as well as external factors that may be subject of broader policy decisions (e.g. mask wearing). The model is utilized to assess transmission risk as a function of OD flows, planned operations, and factors such as mask-wearing, ventilation, and infection rates. Using actual data from the Massachusetts Bay Transportation Authority (MBTA) Red Line, the paper explores the transmission risk under different infection rate scenarios, both in magnitude and spatial characteristics. The paper assesses the combined impact from viral load related factors and passenger load factors. Increasing frequency can mitigate transmission risk, but cannot fully compensate for increases in infection rates. Imbalanced passenger distribution on different cars of a train is shown to increase the overall system-wide infection probability. Spatial infection rate patterns should also be taken into account during policymaking as it is shown to impact transmission risk. For lines with branches, demand distribution among the branches is important and headway allocation adjustment among branches to balance the load on trains to different branches can help reduce risk.
A Collection and Categorization of Open-Source Wind and Wind Power Datasets
Venue: arXiv cs.LG 2022
Year: 2022

Wind power and other forms of renewable energy sources play an ever more important role in the energy supply of today's power grids. Forecasting renewable energy sources has therefore become essential in balancing the power grid. While a lot of focus is placed on new forecasting methods, little attention is given on how to compare, reproduce and transfer the methods to other use cases and data. One reason for this lack of attention is the limited availability of open-source datasets, as many currently used datasets are non-disclosed and make reproducibility of research impossible. This unavailability of open-source datasets is especially prevalent in commercially interesting fields such as wind power forecasting. However, with this paper we want to enable researchers to compare their methods on publicly available datasets by providing the, to our knowledge, largest up-to-date overview of existing open-source wind power datasets, and a categorization into different groups of datasets that can be used for wind power forecasting. We show that there are publicly available datasets sufficient for wind power forecasting tasks and discuss the different data groups properties to enable researchers to choose appropriate open-source datasets and compare their methods on them.
When, where, and how to add new neurons to ANNs
Venue: arXiv cs.LG 2022
Year: 2022

Neurogenesis in ANNs is an understudied and difficult problem, even compared to other forms of structural learning like pruning. By decomposing it into triggers and initializations, we introduce a framework for studying the various facets of neurogenesis: when, where, and how to add neurons during the learning process. We present the Neural Orthogonality (NORTH*) suite of neurogenesis strategies, combining layer-wise triggers and initializations based on the orthogonality of activations or weights to dynamically grow performant networks that converge to an efficient size. We evaluate our contributions against other recent neurogenesis works across a variety of supervised learning tasks.
Point cloud completion via structured feature maps using a feedback network
Venue: arXiv cs.CV 2022
Year: 2022

In this paper, we tackle the challenging problem of point cloud completion from the perspective of feature learning. Our key observation is that to recover the underlying structures as well as surface details, given partial input, a fundamental component is a good feature representation that can capture both global structure and local geometric details. We accordingly first propose FSNet, a feature structuring module that can adaptively aggregate point-wise features into a 2D structured feature map by learning multiple latent patterns from local regions. We then integrate FSNet into a coarse-tofine pipeline for point cloud completion. Specifically, a 2D convolutional neural network is adopted to decode feature maps from FSNet into a coarse and complete point cloud. Next, a point cloud upsampling network is used to generate a dense point cloud from the partial input and the coarse intermediate output. To efficiently exploit local structures and enhance point distribution uniformity, we propose IFNet, a point upsampling module with a self-correction mechanism that can progressively refine details of the generated dense point cloud. We have conducted qualitative and quantitative experiments on ShapeNet, MVP, and KITTI datasets, which demonstrate that our method outperforms state-of-theart point cloud completion approaches.
Alexa versus Alexa: Controlling Smart Speakers by Self-Issuing Voice Commands
Venue: arXiv cs.CR 2022
Year: 2022

We present Alexa versus Alexa (AvA), a novel attack that leverages audio files containing voice commands and audio reproduction methods in an offensive fashion, to gain control of Amazon Echo devices for a prolonged amount of time. AvA leverages the fact that Alexa running on an Echo device correctly interprets voice commands originated from audio files even when they are played by the device itself -- i.e., it leverages a command self-issue vulnerability. Hence, AvA removes the necessity of having a rogue speaker in proximity of the victim's Echo, a constraint that many attacks share. With AvA, an attacker can self-issue any permissible command to Echo, controlling it on behalf of the legitimate user. We have verified that, via AvA, attackers can control smart appliances within the household, buy unwanted items, tamper linked calendars and eavesdrop on the user. We also discovered two additional Echo vulnerabilities, which we call Full Volume and Break Tag Chain. The Full Volume increases the self-issue command recognition rate, by doubling it on average, hence allowing attackers to perform additional self-issue commands. Break Tag Chain increases the time a skill can run without user interaction, from eight seconds to more than one hour, hence enabling attackers to setup realistic social engineering scenarios. By exploiting these vulnerabilities, the adversary can self-issue commands that are correctly executed 99% of the times and can keep control of the device for a prolonged amount of time. We reported these vulnerabilities to Amazon via their vulnerability research program, who rated them with a Medium severity score. Finally, to assess limitations of AvA on a larger scale, we provide the results of a survey performed on a study group of 18 users, and we show that most of the limitations against AvA are hardly used in practice.
Information and communication technology initiatives for knowledge sharing in agriculture
Venue: arXiv cs.CY 2022
Year: 2022

A survey on status and trends of information and communication technologies (ICT) use for knowledge sharing in agriculture was attempted. Among asian countries, India comes under the second next category after the advanced user category comprising Japan, South Korea and Taiwan. Both profit-motive and business augmentation on one hand and community services and rural welfare on the other have been the objectives of ICT-based models in agriculture in India. The ICT endeavours for agriculture belong to a wide array of agencies, viz private sector, public sector, self-help groups and NGOs, and also include combined endeavours. e-Learning is being increasingly resorted to both in (i) in campus or 'presence' mode, and (ii) 'distance' mode. Its use is gradually easing-out the stakeholders from the stranglehold of the inter-deterrence of the 3 arms of the 'Iron Triangle', viz (i) quality, (ii) access, and (iii) cost. The social groups having less mobility are poised to benefit more from this mode of education. This could also be one of the potent tools to bring about gender mainstreaming. e-Learning is being integrated into the existing organizational and educational structure as a hybrid system that can be called 'ICT-supported learning'. Connectivity, content development, infrastructure development, faculty developmeat, need assessment on a continuum, linking the node3 and formation of consortia etc. are the areas identified that need to be supported and developed.
Necessary and sufficient condition for a generic 3R serial manipulator to be cuspidal
Venue: arXiv cs.RO 2022
Year: 2022

Cuspidal robots can travel from one inverse kinematic solution to another without meeting a singularity. The name cuspidal was coined based on the existence of a cusp point in the workspace of 3R serial robots. The existence of a cusp point was proved to be a necessary and sufficient condition for orthogonal robots to be cuspidal, but it was not possible to extend this condition to non-orthogonal robots. The goal of this paper is to prove that this condition stands for any generic 3R robot. This result would give the designer more flexibility. In the presented work, the geometrical interpretation of the inverse kinematics of 3R robots is revisited and important observations on the nonsingular change of posture are noted. The paper presents a theorem regarding the existence of reduced aspects in any generic 3R serial robot. Based on these observations and on this theorem, we prove that the existence of a cusp point is a necessary and sufficient condition for any 3R generic robot to be cuspidal.
Making a Radical Misogynist: How online social engagement with the Manosphere influences traits of radicalization
Venue: arXiv cs.SI 2022
Year: 2022

The algorithms and the interactions facilitated by online platforms have been used by radical groups to recruit vulnerable individuals to their cause. This has resulted in the sharp growth of violent events and deteriorating online discourse. The Manosphere, a collection of radical anti-feminist communities, is one such group which has attracted attention due to their rapid growth and increasingly violent real world outbursts. In this paper, we examine the social engagements between Reddit users who have participated in feminist discourse and the Manosphere communities on Reddit to understand the process of development of traits associated with the adoption of extremist ideologies. By using existing research on the psychology of radicalization we track how specific types of social engagement with the Manosphere influence the development of traits associated with radicalization. Our findings show that: (1) participation, even by the simple act of joining the Manosphere, has a significant influence on the language and outlook traits of a user, (2) Manosphere elites are extremely effective propagators of radical traits and cause their increase even outside the Manosphere, and (3) community perception can heavily influence a user's behavior. Finally, we examine how our findings can help draft community and platform moderation policies to help mitigate the problem of online radicalization.
Fast Dynamic Updates and Dynamic SpGEMM on MPI-Distributed Graphs
Venue: arXiv cs.DC 2022
Year: 2022

Sparse matrix multiplication (SpGEMM) is a fundamental kernel used in many diverse application areas, both numerical and discrete. For example, many algebraic graph algorithms rely on SpGEMM in the tropical semiring to compute shortest paths in graphs. Recently, SpGEMM has received growing attention regarding implementations for specific (parallel) architectures. Yet, this concerns only the static problem, where both input matrices do not change. In many applications, however, matrices (or their corresponding graphs) change over time. Although recomputing from scratch is very expensive, we are not aware of any dynamic SpGEMM algorithms in the literature. In this paper, we thus propose a batch-dynamic algorithm for MPI-based parallel computing. Building on top of a distributed graph/matrix data structure that allows for fast updates, our dynamic SpGEMM reduces the communication volume significantly. It does so by exploiting that updates change far fewer matrix entries than there are non-zeros in the input operands. Our experiments with popular benchmark graphs show that our approach pays off. For batches of insertions or removals of matrix entries, our dynamic SpGEMM is substantially faster than the static algorithms in the state-of-the-art competitors CombBLAS, CTF and PETSc.
Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness
Venue: arXiv cs.CY 2022
Year: 2022

Much of machine learning research focuses on predictive accuracy: given a task, create a machine learning model (or algorithm) that maximizes accuracy. In many settings, however, the final prediction or decision of a system is under the control of a human, who uses an algorithm's output along with their own personal expertise in order to produce a combined prediction. One ultimate goal of such collaborative systems is "complementarity": that is, to produce lower loss (equivalently, greater payoff or utility) than either the human or algorithm alone. However, experimental results have shown that even in carefully-designed systems, complementary performance can be elusive. Our work provides three key contributions. First, we provide a theoretical framework for modeling simple human-algorithm systems and demonstrate that multiple prior analyses can be expressed within it. Next, we use this model to prove conditions where complementarity is impossible, and give constructive examples of where complementarity is achievable. Finally, we discuss the implications of our findings, especially with respect to the fairness of a classifier. In sum, these results deepen our understanding of key factors influencing the combined performance of human-algorithm systems, giving insight into how algorithmic tools can best be designed for collaborative environments.
Iterated Belief Change, Computationally
Venue: arXiv cs.AI 2022
Year: 2022

Iterated Belief Change is the research area that investigates principles for the dynamics of beliefs over (possibly unlimited) many subsequent belief changes. In this paper, we demonstrate how iterated belief change is connected to computation. In particular, we show that iterative belief revision is Turing complete, even under the condition that broadly accepted principles like the Darwiche-Pearl postulates for iterated revision hold.
Developing Imperceptible Adversarial Patches to Camouflage Military Assets From Computer Vision Enabled Technologies
Venue: arXiv cs.CV 2022
Year: 2022

Convolutional neural networks (CNNs) have demonstrated rapid progress and a high level of success in object detection. However, recent evidence has highlighted their vulnerability to adversarial attacks. These attacks are calculated image perturbations or adversarial patches that result in object misclassification or detection suppression. Traditional camouflage methods are impractical when applied to disguise aircraft and other large mobile assets from autonomous detection in intelligence, surveillance and reconnaissance technologies and fifth generation missiles. In this paper we present a unique method that produces imperceptible patches capable of camouflaging large military assets from computer vision-enabled technologies. We developed these patches by maximising object detection loss whilst limiting the patch's colour perceptibility. This work also aims to further the understanding of adversarial examples and their effects on object detection algorithms.
Functional Connectivity Based Classification of ADHD Using Different Atlases
Venue: arXiv cs.CV 2022
Year: 2022

These days, computational diagnosis strategies of neuropsychiatric disorders are gaining attention day by day. It's critical to determine the brain's functional connectivity based on Functional-Magnetic-Resonance-Imaging(fMRI) to diagnose the disorder. It's known as a chronic disease, and millions of children amass the symptoms of this disease, so there is much vacuum for the researcher to formulate a model to improve the accuracy to diagnose ADHD accurately. In this paper, we consider the functional connectivity of a brain extracted using various time templates/Atlases. Local-Binary Encoding-Method (LBEM) algorithm is utilized for feature extraction, while Hierarchical- Extreme-Learning-Machine (HELM) is used to classify the extracted features. To validate our approach, fMRI data of 143 normal and 100 ADHD affected children is used for experimental purpose. Our experimental results are based on comparing various Atlases given as CC400, CC200, and AAL. Our model achieves high performance with CC400 as compared to other Atlases
R2-D2: Repetitive Reprediction Deep Decipher for Semi-Supervised Deep Learning
Venue: arXiv cs.CV 2022
Year: 2022

Most recent semi-supervised deep learning (deep SSL) methods used a similar paradigm: use network predictions to update pseudo-labels and use pseudo-labels to update network parameters iteratively. However, they lack theoretical support and cannot explain why predictions are good candidates for pseudo-labels in the deep learning paradigm. In this paper, we propose a principled end-to-end framework named deep decipher (D2) for SSL. Within the D2 framework, we prove that pseudo-labels are related to network predictions by an exponential link function, which gives a theoretical support for using predictions as pseudo-labels. Furthermore, we demonstrate that updating pseudo-labels by network predictions will make them uncertain. To mitigate this problem, we propose a training strategy called repetitive reprediction (R2). Finally, the proposed R2-D2 method is tested on the large-scale ImageNet dataset and outperforms state-of-the-art methods by 5 percentage points.